John Mandrola, MD Profile picture
Aug 20 7 tweets 4 min read
#TWICPodcast update

You may have seen one of >3000k journal articles describing #obesity paradox: high BMI is protective in, say, AF or Heart failure

That is so weird, b/c exactly zero docs tell normal-weight pts with AF or HF to eat Cheetos or donuts

How can this be?
🧵
Well, here's the deal: there is NO paradox

It's all due to systemic bias

Like studying the r-ship of IQ and sports ability but only in students at an elite college. Here, you'd find an inverse r-ship

The problem is a form of selection bias called collider bias
2/
It is a huge problem and it comes from the choice of which patients to select from

@StevenStovitz showed how it happens in this elegant paper > Please read and save.

Here obesity looks protective for childhood diabetes!

Not!

pubmed.ncbi.nlm.nih.gov/29374028/
I've been trying for years to understand collider bias and I think, emphasis on think, I mostly get it now.

See if you agree> #TWICpodcast podcasts.apple.com/us/podcast/aug…

@theheartorg link medscape.com/viewarticle/97…

BTW -- collider bias featured prominently in many COVID studies
My friend @chadinabhan also has an excellent podcast on collider bias -- with Lars Andersen

podcasts.apple.com/us/podcast/col…
What's even more weird to me is this:

Why are there thousands of systemically flawed papers sitting in the medical literature purporting the benefits of overweight or obesity?

That's a real question
Another great citation is when @_MiguelHernan and colleagues uncovered the systemic bias in the low-birth weight paradox in moms who smoke
academic.oup.com/aje/article/16…

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More from @drjohnm

Jun 19
I want to highlight a recent meta-analysis of the sham-controlled renal denervation trials #hypertension

From @maahmed1218 @AndrewFoy82 and others at Penn State.

Free access x 40 days here: sciencedirect.com/science/articl…

Comments >
They study the 10 sham-controlled trials of RDN. About 1500 patients.

Mean fu (and this is a limitation) = 4 months.

Primary outcomes of the meta-analysis: the *raw mean differences* in 24 hour ambulatory, daytime ambulatory, nighttime, and office SBP and DBP
Results are clear: RDN does indeed reduce BP.

They found statistically sig reductions in all categories accept nocturnal BP.

Here are the differences in 24 hour ambulatory SBP and office SBP.
Read 11 tweets
Jul 18, 2021
My colleague has an interesting idea on vax persuasion

But... Scott Alexander IDs a problem. All 4 folks he suggests are super smart & competent but they aren't optimized only for being correct, they are also optimized for keeping power.

See astralcodexten.substack.com/p/webmd-and-th…
1/
I discussed my experience in clinic with vaccine hesitant patients on This Week in Cardiology podcast
podcasts.apple.com/us/podcast/jul…

Point 1: ... patients don't make decisions like odds-ratio-calculating robots. People feel risk.

Some feel more risk from the vaccine than COVID
Point 2: I don't think the average cardiac patient should feel that way and I do my best to persuade them that the clear benefit-harm choice is to get vaccinated.

Yet I make the same argument for anticoagulants, tobacco cessation etc and still many pts don't heed my advice.
Read 6 tweets
May 16, 2021
When sacubitril/valsartan crushed it in 2014 in PARADIGM-HF for HFrEF, there were a few voices of criticism.

At #ACC21 the PARADISE-MI of entresto vs ramipril in post-MI patients w heart failure failed to show significance.

My summary
medscape.com/viewarticle/95…

Thread/
The specific message is obviously that based on this study, sac/val has no role over ACE/ARB in post-MI patients with heart failure and LV dysfunction.

We cannot ignore costs of care. And low BP was worse in the ARNI group.
The larger message--there is nearly always a larger message--is that we may have over-estimated this drug class.

It missed significance now in 2 of 3 outcome trials.
In PARAGON-HF of HFpEF and now PARADISE-MI.

Re PARADIGM, we know large effect sizes often don't replicate
Read 5 tweets
Mar 13, 2021
Pure gold in this convo on #covid19

Why has @youyanggu been so accurate modeling?

Go to 39-minute mark and replay his words 100x

“When the data do not fit what I expected, I change my beliefs”

If only this was allowed in COVID science!

Brief thread 👇
This principles in this podcast are so darn important for critical appraisal

@youyanggu lack of prior infectious disease knowledge is a feature not a bug.

It allows a dispassionate interpretation of evidence.

Say it w me: **content expertise is over-rated!**

2/
I’m all about Bayes, but the novel-ness of COVID-19, and the fact that it’s a once-in-a-lifetime pandemic, should reduce (or eliminate) any prior beliefs.

I dare say the frequentist-like approach may have been better for Covid.

4/
Read 5 tweets
Mar 12, 2021
Excited to share our recently published point-counterpoint paper on halting implantation of the @bostonsci subcutaneous-ICD

The paper is now open access.

A brief thread
Background:
I have used the S-ICD. It has *potential* advantages over the traditional trans-venous ICD system

But it is the newcomer. A #MedicalConservative believes proponents of new stuff must show more than *potential* benefits over the tried and true.
Recent Issues w the S-ICD:

In the first part of the commentary @bogdienache and I focus our argument around:

-Two Class 1 FDA recalls of the S-ICD
-Serious concerns over the PRAETORIAN trial-- the only trial RCT comparing transvenous ICD (standard) to S-ICD
Read 11 tweets
Jan 30, 2021
Ok at the request of @PerPersvensson let's do a
Bayes Analysis of the HERS trial circa JAMA 1999

Estrogen/progesterone vs Placebo for reduction of MI/CV death.

Here are the events to put in the 2x2 table and the published CI and RR

First just look at the data. No diff in outcomes and wide CI from 20% benefit to 22% harm.

Going to be hard for prior beliefs to budge our posterior

Posterior is jargon for after seeing the data. Image
Goodman has a nice paper on using Bayes courses.botany.wisc.edu/botany_940/06E…

First step is to calculate a likelihood ratio. We do this in our heads all the time as doctors.

The LR is called the Bayes Factor and it is simply the ratio of the probability of the null H over the alternative H Image
Read 18 tweets

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